Risk Analysis of Blocked Rate Predictions for SDN Load Balancing Using Monte Carlo Simulation

The emergence of large data centers and virtualization needs better and smarter solutions for traffic scheduling and load balancing. Data centers benefit from SDN regarding centralized monitoring and management for traffic routing. In general, the traffic in the data center environment can be classified as elephant and mice flow. Researchers showed that there is a significant amount of data carried over elephant flows; therefore, it should be conserved and maintained thoroughly. In this work, we introduce a stochastic performance evaluation model for estimating blocked rate prediction and risk analysis of the elephant flows for a load balancing data center with fat-tree topology using the SDN paradigm. The general procedure of the evaluation includes the estimation of the distribution of the path available bandwidth, including bandwidth error tolerance. The proposed model relies on Monte Carlo simulation to generate future prediction behavior of the load balancing technique. The achieved results examined with Value at Risk (VaR) along with statistics to percept the complete picture of the load balancing behavior.